Machine learning For Smarter Manufacturing & its Fundamentals

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Machine Learning
F o r S m a r t e r M a n u f a c t u r i n g a n d i t s F u n d a m e n t a l s
S u c h i t G a i k w a d
M . S c S t a t i s t i c s

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Introduction : What is Machine Learning ?
Machine learning (ML) is the study of
computer algorithms that improve
automatically through experience. It is seen as
a subset of AI (Artificial Intelligence). Machine
learning algorithms build a Statistical
model based on sample data, known as
"training data", in order to make predictions or
decisions without being explicitly programmed
to do so. Machine learning algorithms are
used in a wide variety of applications, such
as email filtering and computer vision
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Lets Understand with Example
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• After First
Attempt You
realized that
you are
applying too
much force
• After Second
Attempt Your
are closer to
target but
you need to
increase the
throw angel
• This way you
are learning
something at
every attempt
and
improving the
end result

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You can Do Something Similar with
machine Too
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• You can
Program a
machine from
Every attempt or
Experience or
Data Point and
there by
improve the
outcome
• Machine
Learning
Provides
Computer with
the ability to
learn without
being explicitly
programmed

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Evolution of Machine Learning
.
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1950s 1960s 1970s 1980s 1990s 2000s 2010s
Pioneering machine
learning research is
conducted using
simple algorithms
Bayesian methods are
introduced for
probabilistic inference
in machine learning
‘AI Winter’ Caused by
pessimism about
machine learning
effectiveness
Neural network
became popular
Work on machine
learning shifts from a
knowledge-driven
approach to a data-
driven approach
Support Vector Clustering
and other Kernel methods
and Unsupervised machine
learning method become
widespread
Deep Learning Becomes
Feasible, which leads to
machine learning becoming
integral to many widely used
software and applications.

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1950’s
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• In 1952 AI Pioneer Arthur Samuel he was
working for IBM & He Created First
Learning Machine In Fact he was the First
Person to popularized the term machine
learning and his System could learn to play
checkers or Draft with help of Simple
Algorithm.

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1960’s
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• With the Help Of Bayesian
methods to solve the
probabilistic inference in ML
• For Ex: Given the test is
positive, what is the probability
that person has cancer

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1970’s
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• In the history of Machine
Learning, an AI winter is a
period of reduced funding
and interest in Machine
Learning research.
• Which Caused by
pessimism about machine
learning effectiveness

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1980’s
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• A neural network can be
trained to produce outputs
that are expected, given a
particular input.
• For Ex: Stock Market
Prediction.

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1990’s
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• Scientists begin creating
programs for computers to
analyze large amounts
of data and draw conclusions
– or "learn" – from the
results.
• Deep Blue was a chess -
playing computer developed
by IBM in 1997. It is known
for being the first computer
chess-playing system to win
both a chess game and a
chess match against a
reigning world champion
under regular time controls

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2000’s
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• With help of Kernel
Methods
and Unsupervised
Machine Learning
methods in 2006 Geoffrey
Hinton Publish are
research recognizing
Handwritten digits with
an Accuracy grater than
98% that when Machine
Learning Explosion
Starts.

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2010’s
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• Deep Learning Helps which
Film You Want to watch
Showing in BookmyShow
Applications.
• Recommendation Of
product on Amazon and
Flipkart Based on your
Requirements
• Now at this Moment
Machine learning is now
part of life its is every
were.

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Role of Machine Learning in Manufacturing Industry
• Machine Learning plays an important role in
enhancing the quality of the manufacturing
process.
• Deep-learning neural networks can help in the
availability, performance, quality of assembly
equipment, and weaknesses of the machine.
• Data has become a valuable resource, and it’s
cheaper than ever to capture and store. Through
the use of artificial intelligence,
specifically Process-Based Machine Learning,
manufacturers can use data to significantly impact
their bottom line by greatly improving production
efficiency, product quality, and employee safety. 13

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Enabling Predictive Quality Analytics with Machine
Learning
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• Preventing downtime is not the only goal that
industrial AI can assist us with. The quality
of output is crucial and product quality
deterioration can also be predicted using
Machine Learning. Knowing beforehand that
the quality of products being manufactured
is destined to drop prevents the wastage of
raw materials and valuable production
time.
• Machine Learning can be split into two main
techniques – Supervised and Unsupervised
machine learning.

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Lets Understand How Machine Learning is
Classified
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Supervised Machine Learning
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• In manufacturing use cases, supervised
machine learning is the most commonly
used technique since it leads to a
predefined target: we have the input data;
we have the output data; and we’re
looking to map the function that connects
the two variables .
• Supervised machine learning demands a
high level of involvement – data input,
data training, defining and choosing
algorithms, data visualizations, and so
on. The goal is to construct a mapping
function with a level of accuracy that
allows us to predict outputs when new
input data is entered into the system .

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Supervised Machine Learning Flow Diagram
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Supervised Machine Learning
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• Initially, the algorithm is fed from a training dataset, and by
working through iterations, continues to improve its
performance as it aims to reach the defined output. The
learning process is completed when the algorithm reaches an
acceptable level of accuracy.
• In manufacturing, one of the most powerful use cases for
Machine Learning is Predictive Maintenance, which can be
performed using two Supervised Learning approaches:
Classification and Regression .
• These 2 approaches share the same goal: to map a relationship
between the input data (from the manufacturing process) and
the output data (known possible results such as part failure,
overheating etc .)

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Classification
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• When data exists in w ell -defined categories,
C lassif ication can be used. A n example of
C lassif ication t hat w e’re all f amiliar w ith is
t he email f ilt er algorit hm t hat decides
w hether an email should be sent t o our spam
f older, or not . C lassif ication is limit ed t o a
Boolean value response, but can be very
usef ul since only a small amount of dat a is
needed t o achieve a high level of accuracy.
• Predict ive Maint enance makes use of mult i -
class classif icat ion since t here are mult iple
possible causes for the failure of a machine
or component . These are possible out comes
t hat are classif ied as pot ent ial equipment
issues, calculat ed using a number of
variables including machine healt h, risk
levels and possible reasons f or malf unct ion.

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Classification
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• In machine learning, common Classification algorithms include
Decision Trees, k -Nearest Neighbour (kNN) & Support Vector
Machine (SVM)

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Regression
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• R e g r e s s i o n i s u s e d w h e n d a t a e x i s t s
w i t h i n a r a n g e ( e g . t e m p e r a t u r e , w e i g h t ) ,
w h i c h i s o f t e n t h e c a s e w h e n d e a l i n g
w i t h d a t a c o l l e c t e d f r o m s e n s o r s .
• I n m a n u f a c t u r i n g , r e g r e s s i o n c a n b e
u s e d t o c a l c u l a t e a n e s t i m a t e f o r t h e
R e m a i n i n g U s e f u l L i f e ( R U L ) o f a n a s s e t .
T h i s i s a p r e d i c t i o n o f h o w m a n y d a y s o r
c y c l e s w e h a v e b e f o r e t h e n e x t
c o m p o n e n t / m a c h i n e / s y s t e m f a i l u r e .
• F o r r e g r e s s i o n , t h e m o s t c o m m o n l y u s e d
m a c h i n e l e a r n i n g a l g o r i t h m i s L i n e a r
R e g r e s s i o n , b e i n g f a i r l y q u i c k a n d
s i m p l e t o i m p l e m e n t , w i t h o u t p u t t h a t i s
e a s y t o i n t e r p r e t . A n e x a m p l e o f l i n e a r
r e g r e s s i o n w o u l d b e a s y s t e m t h a t
p r e d i c t s t e m p e r a t u r e , s i n c e t e m p e r a t u r e
i s a c o n t i n u o u s v a l u e w i t h a n e s t i m a t e
t h a t w o u l d b e s i m p l e t o t r a i n .

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Linear Regression
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• Linear Regression – A modeling
function that assumes a linear
relationship between the input
variables x and the single output
variable y and creates a trend-line
(prediction model) using the
formula y=ax+b
• For Example regression model for
variables like fabric Gauge and
Square meter weight
• But with machine Learning we can
make this algorithm and use to
analyze every single day.
y = 1.2802x - 0.2669
R² = 0.9707
1.18
1.19
1.2
1.21
1.22
1.23
1.24
1.25
1.26
1.27
1.28
1.13 1.14 1.15 1.16 1.17 1.18 1.19 1.2 1.21
SquaremeterWeight
Fabric Gauge
LINEAR REGRESSION

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Logistic Regression
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• Logistic Regression –
Also known as
exponential (x2, x3,
…, xn) or polynomial
(y=ax2+bx+c) regression,
is similar to linear
regression but the trend-
line (y=1/(1+ex)) is
assumed to be of a
higher order

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Unsupervised Machine Learning
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• With Supervised machine learning we start off by working from
an expected outcome and train the algorithm accordingly.
Unsupervised learning is suitable for cases where the outcome
is not yet known.

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Clustering
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• In some cases, not only will
the outcome be unknown to
us, but information describing
the data will also be lacking
(data labels). By creating
clusters of input data points
that share certain attributes, a
Machine Learning algorithm
can discover underlying
patterns.
• Clustering can also be used to
reduce noise (irrelevant
parameters within the data)
when dealing with extremely
large numbers of variables.

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Classification Vs. Clustering
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Artificial Neural Networks
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• In the manufacturing sector, Artificial Neural Networks are
proving to be an extremely effective Unsupervised learning tool
for a variety of applications including production process
simulation and Predictive Quality Analytics.
• The basic structure of the Artificial Neural Network is loosely
based upon how the human brain processes information using
its network of around 100 billion neurons, allowing for
extremely complex and versatile problem solving.
• This ability to process a large number of parameters through
multiple layers makes Artificial Neural Networks very suitable
for the variable -rich and constantly changing processes
common to manufacturing. Moreover, once properly trained, an
Artificial Neural Network can demonstrate a high level of
accuracy when creating predictions regarding the mechanical
properties of processed products, enabling cuts in the cost of
raw materials.

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Artificial Neural Networks
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• This ability to process a large
number of parameters through
multiple layers makes Artificial
Neural Networks very suitable
for the variable -rich and
constantly changing processes
common to manufacturing.
Moreover, once properly
trained, an Artificial Neural
Network can demonstrate a
high level of accuracy when
creating predictions regarding
the mechanical properties of
processed products, enabling
cuts in the cost of raw
materials.

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Programming Languages used in Machine Learning
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• Machine learning is writing code that lets
machines make decisions based on pre -
defined algorithms on provided datasets .
• So, what is the most popular programming
language for machine learning? Almost any
programming language can be used to write
ML based applications. However, writing
every algorithm from scratch is a time -
consuming process. The best suited
programming language is the one that
comes with pre -built libraries and have
advanced support of data science and data
models.

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Most popular programming languages for
machine learning.
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• Python
• C++
• Java
• JavaScript
• C#
• R
• Julia
• GO
• TypeScript
• Scala

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Python
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• P yt h o n i s o n e o f t h e m o s t p o p u l a r
p r o g r a m m i n g l a n g u a g e s o f r e c e n t
t i m e s . P yt h o n , c r e a t e d b y G u i d o
va n R o s s u m i n 1 9 9 1 , i s a n o p e n -
s o u r c e , h i g h - l e ve l , g e n e r a l
p u r p o s e p r o g r a m m i n g l a n g u a g e .
P yt h o n i s a d yn a m i c p r o g r a m m i n g
l a n g u a g e w h i c h s u p p o r t s o b j e c t -
o r i e n t e d , i m p e r a t i ve , f u n c t i o n a l
a n d p r o c e d u r a l d e ve l o p m e n t
p a r a d i g m s . P yt h o n i s ve r y
p o p u l a r i n m a c h i n e l e a r n i n g
p r o g r a m m i n g .
• P yt h o n i s o n e o f t h e f i r s t
p r o g r a m m i n g l a n g u a g e s t h a t g o t
t h e s u p p o r t o f m a c h i n e l e a r n i n g
vi a a va r i e t y o f l i b r a r i e s a n d
t o o l s .
• S c i k i t a n d Te n s o r F l o w a r e t w o
p o p u l a r m a c h i n e l e a r n i n g
l i b r a r i e s a va i l a b l e t o P yt h o n
d e ve l o p e r s .

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Benefits of using Python
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• Python is widely considered as the preferred language for
teaching and learning Ml (Machine Learning). Few simple
reasons are:
• It’s simple to learn. As compared to c, c++ and Java the
syntax is simpler and Python also consists of a lot of code
libraries for ease of use.
• Though it is slower than some of the other languages, the
data handling capacity is great.
• Open Source! – Python along with R is gaining momentum and
popularity in the Analytics domain since both of these
languages are open source.
• Capability of interacting with almost all the third party
languages and platforms.

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Benefits of Machine Learning for Manufacturing
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• The introduction of AI and Machine
Learning to industry represents a sea
change with many benefits that can
result in advantages well beyond
efficiency improvements, opening
doors to new business opportunities .
• Some of the direct benefits of
Machine Learning in manufacturing
include…

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• Cost reduction through Predictive
Maintenance. PdM leads to less maintenance
activity, which means lower labor costs and
reduced inventory and materials wastage.
• Predicting Remaining Useful Life (RUL).
Knowing more about the behavior of
machines and equipment leads to creating
conditions that improve performance while
maintaining machine health. Predicting RUL
does away with “unpleasant surprises” that
cause unplanned downtime.
• Improved supply chain management through
efficient inventory management and a well
monitored and synchronized production
flow.

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• Improved Quality Control with
actionable insights to constantly
raise product quality.
• Improved Human-
Robot collaboration improving
employee safety conditions and
boosting overall efficiency.
• Consumer-focused manufacturing –
being able to respond quickly to
changes in the market demand.

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Thank You

Machine learning For Smarter Manufacturing & its Fundamentals

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